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Rob
robthepirate
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TRELLIS is now the highest ranked open-source model in the 3D Arena Leaderboard, surpassing InstantMesh
dylanebert/3d-arena
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Effortlessly stay up-to-date with AI research trends using a new AI tool, "AI Paper Reviewer" !!
It analyzes a list of Hugging Face Daily Papers(w/ @akhaliq ) and turn them into insightful blog posts. This project leverages Gemini models (1.5 Pro, 1.5 Flash, and 1.5 Flash-8B) for content generation and Upstage Document Parse for parsing the layout and contents.
blog link: https://deep-diver.github.io/ai-paper-reviewer/
Also, here is the link of GitHub repository for parsing and generating pipeline. By using this, you can easily build your own GitHub static pages based on any arXiv papers with your own interest!
: https://github.com/deep-diver/paper-reviewer
It analyzes a list of Hugging Face Daily Papers(w/ @akhaliq ) and turn them into insightful blog posts. This project leverages Gemini models (1.5 Pro, 1.5 Flash, and 1.5 Flash-8B) for content generation and Upstage Document Parse for parsing the layout and contents.
blog link: https://deep-diver.github.io/ai-paper-reviewer/
Also, here is the link of GitHub repository for parsing and generating pipeline. By using this, you can easily build your own GitHub static pages based on any arXiv papers with your own interest!
: https://github.com/deep-diver/paper-reviewer
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New app built based on https://huggingface.co./docs/transformers.js and
minishlab/potion-6721e0abd4ea41881417f062!
It uses the super performant CPU-only models to calculate semantic similarity fully client-side based on Excel or CSV tables.
- App: https://do-me.github.io/semantic-similarity-table/
- Code: https://github.com/do-me/semantic-similarity-table
It uses the super performant CPU-only models to calculate semantic similarity fully client-side based on Excel or CSV tables.
- App: https://do-me.github.io/semantic-similarity-table/
- Code: https://github.com/do-me/semantic-similarity-table
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🏁 Now it is possible to chat with telemetry data from real Formula 1 races!
This is an AI-powered solution for analyzing and generating detailed reports on Formula 1 racing sessions. This project combines the power of ReAct agents from LangChain with a RAG approach to pull data from a SQL database.
At the core of this system is a text-to-SQL capability that allows users to ask natural language questions about various aspects of F1 races, such as driver performance, weather impact, race strategies, and more. The AI agent then queries the database, processes the information, and generates comprehensive reports tailored to the user's needs.
The reports can be exported in various formats, making it easy to share insights with team members, race fans, or the broader motorsports community.
(The project is in beta, some erros may occur)
Check it out:
- Draichi/Formula1-race-debriefing
- https://github.com/Draichi/formula1-AI
This is an AI-powered solution for analyzing and generating detailed reports on Formula 1 racing sessions. This project combines the power of ReAct agents from LangChain with a RAG approach to pull data from a SQL database.
At the core of this system is a text-to-SQL capability that allows users to ask natural language questions about various aspects of F1 races, such as driver performance, weather impact, race strategies, and more. The AI agent then queries the database, processes the information, and generates comprehensive reports tailored to the user's needs.
The reports can be exported in various formats, making it easy to share insights with team members, race fans, or the broader motorsports community.
(The project is in beta, some erros may occur)
Check it out:
- Draichi/Formula1-race-debriefing
- https://github.com/Draichi/formula1-AI
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